Adaptive Control of Vehicular Traffic

ABSTRACT

A traffic control system for controlling traffic at interconnected intersections is provided, where the system comprises a receiver that receives traffic data that indicates states of vehicles approaching an intersection of the interconnected intersections and directions of the vehicles exiting the intersection. Further, the system comprises a processor that determines intersection crossing times and velocities of vehicles approaching the intersection by minimizing at least one of a total travel time or a maximum travel time of the vehicles for crossing the intersection. The contribution of each vehicle of the vehicles approaching the intersection in the at least one of a total travel time or a maximum travel time is weighted based on directions of the vehicles and traffic at next intersection. Further, the system comprises a transmitter that transmits the intersection crossing times and velocities to the vehicles exiting the intersection for controlling the traffic at the interconnected intersections.

TECHNICAL FIELD

The present invention relates generally to vehicle control, and moreparticularly to methods and apparatus for adaptive control of vehiculartraffic at interconnected intersections.

BACKGROUND

Traffic congestion is a significant problem in many locales throughoutthe world with costs that include lost hours, environmental threats, andwasted fuel consumption. For instance, the costs of traffic congestioncan be measured in hundreds of dollars per capita per year in the UnitedStates. To that end, there is a need to reduce traffic congestion and/orimprove any other traffic goal. In past, congestion has been improved bychanging traffic light timing and building new roads. With the advent ofwireless technology and connected components, it has become possible tocommunicate with vehicles and road infrastructure to make real-timedecisions that improve traffic.

For example, Internet of Vehicles (IoV), which is a convergence ofmobile Internet and Internet of Things (IoT), enables informationgathering, information sharing and information processing to effectivelyguide and supervise vehicles. The IoV is uniquely different from the IoTbecause of mobility, safety, Vehicle to Everything (V2X) communication,energy conservation, security attacks, etc. In the IoV, information andcommunication technologies are applied in the infrastructure, vehiclesand users to manage traffic and vehicle mobility. The IoV aims toprovide innovative services and control for traffic management and toenable users to be better informed for making safer, more coordinated,and smarter use of transportation networks.

With the development of IoV, vehicles such as connected and autonomousvehicles (CAVs) are also emerging. These types of vehicles arecontrolled based on advanced technologies using communications, sensors,optimal control techniques, etc. The advance control mechanisms alsooptimize control efficiency by making real-time optimal controldecisions. Further, the emergence of autonomous vehicles has drawnattentions to on-board control of the autonomous vehicles. The on-boardcontrol uses communication data, advance devices such as radar, lidar,camera, GPS, etc., and artificial intelligence (AI) technologies toautomatically control vehicle mobility.

The rapid development of IoV and CAV has contributed to development ofsmart city infrastructure or urban transportation system. In the urbantransportation system, intersections are crucial area for trafficcontrol. In traffic scenarios, the vehicles are required to frequentlystop at traffic congestion at the intersections. The frequent stops atthe intersections cause delays that frustrate users of the vehicles.This also increases wastage of fuel as well as increase in pollution. Itis also noteworthy that the intersection traffic congestion is alsoaffected by traffic at interconnected intersections. The trafficcongestion increases if traffic control at the interconnectedintersections is not coordinated.

To that end, there is a need to control vehicular mobility at theintersections to improve the traffic at interconnected intersections.More specifically, there is a need to coordinate traffic control at theinterconnected intersections, while preventing the vehicles to halt inorder to pass an intersection or a highway merging point. Such trafficcongestion may be managed by using model based traffic control throughArtificial Intelligence (AI) techniques. However, model based trafficcontrol may be a complicated process. Traffic systems are difficult tomodel as it requires human involvement. Hence, some methods usedata-driven techniques, such as reinforcement learning for the trafficcontrol. However, the data-driven techniques represent aggregation oftraffic data in a manner that is difficult to change abruptly inemergency and other situations when there is a need to tune the trafficcontrol.

Accordingly, there is still a need to provide a method for controllingvehicles traveling at interconnected intersections suitable to beadapted and/or tuned to various situations.

SUMMARY

It is an object of some embodiments to provide a system and a method fortraffic control for vehicles traveling at roads of interconnectedintersections. Additionally, or alternatively, it is another object ofsome embodiments to optimize control efficiency by making real-timeoptimal control decisions aimed at reducing traffic congestion on theinterconnected intersections. Additionally, or alternatively, it isanother object of some embodiments to provide such a control method thatis tunable for different traffic situations, conditions, and controlobjectives.

Additionally, or alternatively, it is another object of some embodimentsto optimize control efficiency by making real-time optimal controldecisions, while preventing vehicles to stop from crossing theintersection and/or highway merging point. Additionally, oralternatively, it is another object of some embodiments to optimizecontrol efficiency of passing the intersection in consideration oftraffic in neighboring intersections.

Some embodiments are based on realization that control, aiming tominimize total travel time of the vehicles crossing an intersection, canreduce traffic congestions and even may allow vehicles to pass theintersection without stopping. Additionally, or alternatively,minimizing a maximum travel time of the vehicles crossing theintersection can also reduce traffic congestions and allow vehicles topass the intersection without stopping. Some embodiments are based onanother realization that control of the vehicles crossing theintersections can be performed by estimating and informing the vehiclesabout their corresponding intersection crossing times and velocitieswhile entering the intersection. In such a manner, the low-levelcontrollers of the vehicles can be used for crossing the intersectionand safety of the crossing can be improved.

Some embodiments are based on realization that tuning of crossing anintersection can be performed by weighing contributions or weights ofdifferent vehicles at neighboring intersections. The weights are used inminimizing the total travel time or the maximum travel time of vehiclesapproaching the intersection. The tuning procedure for estimating theweights of the vehicles can be based on traffic at the next intersectionon a direction of a vehicle after the vehicle crosses the intersection.For example, if weight of vehicle A is less than weight of vehicle B, asa result of such a weighted minimization, the vehicle B is more likelyto have a priority for crossing the intersection over vehicle A. Theweight of the vehicle can be balanced based on destinations of thevehicles after passing the intersection. In such a manner, the weightcan be dynamically adapted. In addition, this configuration allows tofurther modify the weight if necessary, for example, dictated byemergency situation.

To that end, some embodiments minimize the total travel time or maximumtravel by solving a mixed integer linear problem (MILP). Someembodiments are based on realization that different vehicles approachthe intersection at different times and therefore there is a need togroup the vehicles for performing the minimization. There is also needto avoid duplication and/or update of the estimation of the intersectioncrossing times and velocities of the vehicles. The grouped vehiclesenable determining the intersection crossing time and velocity of eachvehicle only once even when situation at the roads approaching theintersection changes.

Some embodiments are based on realization that edge devices, such asroadside unit (RSU) are feasible control points to partition roads of anintersection. To that end, some embodiments partition the roadsapproaching the control point into a sequencing zone and a control zone.The partitioning can be performed by various methods. For example, aroad is partitioned based on state of vehicles, road conditions, roadgeometry. The control zone is adjacent to the intersection and thesequencing zone is adjacent to the control zone. Each zone includessections of multiple roads on which the vehicles are moving towards thecontrol point, such as the intersection or highway merging point. Foreach road, the control zone is a section of a road between a startingpoint of the intersection and ending point of the sequencing zone. Someembodiments determine the intersection crossing times and velocities ofvehicles in the sequencing zone. In such a manner, when the vehicles arein the control zone, their intersection crossing times and velocitiesare known to them and can be used for calculating an optimal trajectoryof a vehicle in the control zone ending the motion in the control zoneat the intersection crossing time with the intersection crossingvelocity. Hence, the intersection crossing times and velocities for thevehicles in the control zone are fixed and not updated.

To consider the intersection crossing times and velocities for thevehicles in the control zone, intersection crossing times and velocitiesfor the vehicles are optimally determined in the sequencing zone. Someembodiments perform such optimization by minimizing weighted totaltravel time or weighted maximum travel time of the vehicles. In someimplementations, to determine the intersection crossing times andvelocities of the vehicles in the sequencing zone, the weighted totaltravel time or the weighted maximum travel time of the vehicles in thesequencing zone and the control zone is minimized while having fixedintersection crossing times and velocities for the vehicles in thecontrol zone.

Some embodiments are based on recognition for a need to determinelengths of the sequencing zone and the control zone. The lengths of thesequencing zone and the control zone can differ for different roads. Thesequencing zone and control zone lengths can be adaptive with an upperbound and a lower bound. An upper bound for the control zone length isdistance between two adjacent intersections. An upper bound for thesequencing zone length is a distance between two adjacent intersectionswithout the control zone length. A maximum distance required toaccelerate a vehicle to a maximum velocity from control zone enteringvelocity using a maximum acceleration velocity and distance needed tostop the vehicle from the control zone entering velocity using a maximumdeceleration velocity, provides a lower bound for the control zonelength. Thus, the control zone length must be long enough such that avehicle can reach any velocity from its control zone entering velocity.The sequencing zone must be long enough such that vehicles can sendtheir status to an edge device, e.g. a road-side unit (RSU) located atthe intersection. The RSU can solve a mixed integer linear problem(MILP) to determine intersection crossing times and velocities. The RSUtransmits the determined intersection crossing times and velocities tothe vehicles. The vehicles determine their optimal motion trajectoriesfor the control zone based on the transmitted intersection crossingtimes and velocities.

Some embodiments are based on the recognition of the complexity of sucha traffic control problem at the interconnected intersections. Forexample, one of issues addressed by some embodiments is an arrangementof the control system configured for real-time traffic control at theinterconnected intersections. For example, some embodiments are based onrecognition that the cloud control is impractical to optimally controlpassing the intersection and/or highway merging point. The cloud controlmay not meet real-time constraint of the safety requirement due to themulti-hop communication delay. In addition, the cloud does not haveinstant information of vehicles, pedestrians and road condition to makeoptimal decision. On the other hand, vehicle on-board control may nothave sufficient information to make optimal decision, e.g., on-boardcontrol of the vehicle does not have information about object movementout of the visible range and cannot receive information from vehiclesoutside of the communication range. In addition, the on-board controlmay also have communication limitation due to the existence of theheterogeneous vehicular communication technologies such as IEEEShort-Range Communications/Wireless Access in Vehicular Environments(DSRC/WAVE) and 3GPP Cellular-Vehicle-to-Anything (C-V2X), e.g., avehicle equipped with IEEE DSRC/WAVE radio cannot communication with avehicle equipped with 3GPP C-V2X radio.

To that end, some embodiments are based on realization that the edgedevices such as the roadside units (RSUs) are feasible control points tomake optimal decision on real-time control of crossing the intersectionor highway merging point due to their unique features such as directcommunication capability with vehicles, road condition knowledge andenvironment view via cameras and sensors. In addition, edge devices at acontrol point such as the intersection or highway merging point can makejoint control decision via real time collaboration and informationsharing. To that end, some embodiments utilize edge devices to realizereal time edge control.

The advance vehicle mobility is controlled by running control methodsusing effective communication data and sensor data. Vehicularenvironment is a highly dynamic environment. Besides the vehiculardynamics, there are unpredictable environment dynamics, such emergencysituation due to random movement of objects such as pedestrians andanimals, sudden events caused by trees and infrastructure. Therefore,the control methods need to be rapidly adaptable to the entireenvironment dynamics. In other words, control methods must be fastenough to reflect the dynamics of vehicular environment. The runtime ofcontrol methods depends on the number of vehicles involved, complexityof control techniques, resources of the control device, etc. The controlmethods include computation of the intersection crossing times andvelocities within a computation time to determine the intersectioncrossing times and velocities. To guarantee the safety, the computationtime needs to be below a threshold. Further, an infrastructure edgedevice, e.g., DSRC/WAVE RSU and C-V2X eNodeB, referred to hereinafterIoV-Edge is used to control vehicle traffic. The IoV-Edge is equippedwith appropriate control techniques, computing resources andcommunication interfaces. For the IoV-Edge, it is impractical todynamically update its control techniques and computation resources.However, it is feasible to adjust the number of vehicles involved toreduce the runtime of the control techniques.

Accordingly, one embodiment discloses a traffic control system forcontrolling traffic at interconnected intersections of roads, whichincludes a receiver configured to receive traffic data indicative ofstates of vehicles approaching an intersection of the interconnectedintersections and directions of the vehicles exiting the intersection; aprocessor configured to determine intersection crossing times andvelocities of vehicles approaching the intersection by minimizing one ofa total travel time of each vehicle of the vehicles or a maximum traveltime of each vehicle of the vehicles for crossing the intersection,wherein a contribution of each vehicle of the vehicles approaching theintersection in the one of a total travel time or a maximum travel timeis weighted based on direction of the corresponding vehicle and trafficat next intersection, such that the minimization uses different weightsfor at least two different vehicles of the vehicles approaching theintersection; and a transmitter configured to transmit the intersectioncrossing times and velocities to the vehicles exiting the intersectionfor controlling the traffic at the interconnected intersections.

Another embodiment discloses a method for controlling traffic atinterconnected intersections of roads, wherein the method uses aprocessor coupled to a receiver configured to receive traffic dataindicative of states of vehicles approaching an intersection of theinterconnected intersections and directions of the vehicles exiting theintersection, wherein the processor is coupled with stored instructionsimplementing the method, wherein the instructions, when executed by theprocessor carry out steps of the method, which includes determiningintersection crossing times and velocities of the vehicles approachingthe intersection by minimizing one of a total travel time of eachvehicle of the vehicles or a maximum travel time of each vehicle of thevehicles for crossing the intersection, wherein a contribution of eachvehicle of the vehicles approaching the intersection in the total traveltime or the maximum travel time is weighted based on direction of thecorresponding vehicle and traffic at next intersection, such that theminimization uses different weights for at least two different vehiclesof the vehicles approaching the intersection; and transmitting theintersection crossing times and velocities to the vehicles exiting theintersection via a transmitter coupled to the processor for controllingthe traffic at the interconnected intersections.

Yet another embodiment discloses a non-transitory computer readablestorage medium embodied thereon a program executable by a processor forperforming a method. The method includes determining intersectioncrossing times and velocities of vehicles approaching an intersection ofinterconnected intersections of roads by minimizing one of a totaltravel time of each vehicle of the vehicles or a maximum travel time ofeach vehicle of the vehicles crossing the intersection based on trafficdata indicative of states of the vehicles approaching the intersectionand directions of the vehicles exiting the intersection, wherein acontribution of each vehicle in the total travel time or the maximumtravel time is weighted based on direction of the corresponding vehicleand traffic at next intersection, such that the minimization usesdifferent weights for at least two different vehicles of the vehiclesapproaching the intersection; and transmitting the intersection crossingtimes and velocities to the vehicles for exiting the intersection via atransmitter coupled to the processor, for controlling the traffic at theinterconnected intersections.

BRIEF DESCRIPTION OF THE DRAWINGS

The presently disclosed embodiments will be further explained withreference to the attached drawings. The drawings shown are notnecessarily to scale, with emphasis instead generally being placed uponillustrating the principles of the presently disclosed embodiments.

FIG. 1 shows a traffic scenario illustrating traffic problems atinterconnected intersections of roads, according to some embodiments.

FIG. 2 shows steps of a method for controlling traffic at interconnectedintersections of roads, according to some embodiments.

FIG. 3 shows a schematic diagram illustrating zone partition inproximity to a road intersection, according to some embodiments.

FIG. 4 shows a schematic diagram illustrating a length of a sequencingzone and a length of a control zone of the road intersection, accordingto some embodiments.

FIG. 5 shows an exemplary representation of information of states ofvehicles approaching an intersection of interconnected intersections anddirections of vehicles exiting the intersection, according to someembodiments.

FIG. 6 shows an exemplary representation of intersection crossing timesand velocities for the vehicles exiting the intersection, according tosome embodiments.

FIG. 7 shows an exemplary representation of information exchange amongIoV-Edges and the vehicles crossing the intersection, according to someembodiments.

FIG. 8 shows an exemplary schematic diagram depicting determination ofweights of vehicles based on directions of the vehicles for minimizationof at least one of a total travel time or maximum travel time of thevehicles, according to some embodiments.

FIG. 9 shows an exemplary schematic diagram of controlling traffic atinterconnected intersections of roads based on intersection crossingtimes and velocities of the vehicles, according to some embodiments.

FIG. 10 shows a block diagram of a control system for controllingtraffic at interconnected intersections, according to some embodiments.

FIG. 11A shows a schematic of a vehicle including a controller incommunication with the control system, according to some embodiments.

FIG. 11B shows a schematic of interaction between a set of control unitsof the vehicle for controlling vehicular mobility, according to someembodiments.

FIG. 11C shows a schematic for determining optimal trajectory for avehicle, according to some embodiments.

FIG. 12 shows a graphical representation depicting comparison of amaximum travel time with respect to an intersection crossing velocityand a maximum travel time with respect to an initial velocity of avehicle entering control zone, according to some embodiments.

FIG. 13 shows a graphical representation depicting impact of weightingparameter in controlling traffic at interconnected intersections,according to some embodiments.

FIG. 14 shows a graphical representation depicting impact of neighboringintersections in controlling traffic at interconnected intersections,according to some embodiments.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,to one skilled in the art that the present disclosure may be practicedwithout these specific details. In other instances, apparatuses andmethods are shown in block diagram form only in order to avoid obscuringthe present disclosure.

As used in this specification and claims, the terms “for example,” “forinstance,” and “such as,” and the verbs “comprising,” “having,”“including,” and their other verb forms, when used in conjunction with alisting of one or more components or other items, are each to beconstrued as open ended, meaning that the listing is not to beconsidered as excluding other, additional components or items. The term“based on” means at least partially based on. Further, it is to beunderstood that the phraseology and terminology employed herein are forthe purpose of the description and should not be regarded as limiting.Any heading utilized within this description is for convenience only andhas no legal or limiting effect.

FIG. 1 shows a traffic scenario 100 illustrating problems atinterconnected intersections of roads, according to some embodiments.The interconnected intersection includes physically interconnectedintersections and communicably interconnected interconnections. Thephysically interconnected intersections are intersections where vehiclestravel from one intersection to another intersection. The communicablyinterconnected interconnections are intersections that share trafficinformation from one intersection to another intersection viacommunication channels.

The traffic scenario 100 shows an intersection 102 and an intersection104 which are interconnected to each other, and also called asinterconnected intersections of roads 106-116, where vehicles 126-146are present on the roads 106-116 of the interconnected intersections.The vehicles (e.g., the vehicles 126-146) may be autonomous,semi-autonomous or manually operated vehicle. Some examples of thevehicles 126-146 include two-wheeler vehicles, such as motor bikes,four-wheeler vehicles, such as cars or more than four-wheel vehicles,such as trucks and the like.

There is further shown a road site unit (RSU) 122 and a RSU 124, a corenetwork 120, and a cloud network 118 to establish an Internet ofVehicles (IoV) environment within the set of vehicles 126-146 on theroads 106-116.

In some embodiments, the traffic scenario 100 corresponds to ametropolitan area, where the roads 106-116 form a large number ofintersections, such as the intersections 102 and 104. In themetropolitan area, traffic conditions at the intersections 102 and 104determine traffic flow because traffic congestion usually starts at anintersection (such as the intersection 104) and propagates to the roads(e.g. 106-116). The traffic conditions at the intersections 102 and 104are interdependent such that a variation at one intersection (i.e. theintersection 104) propagates to other intersections, such as theintersection 102 (also called as a neighboring intersection 102).

Some embodiments are based on a realization that to establishcommunication among different vehicles (e.g., the set of vehicle126-146) in the IoV environment, communication between the cloud network118 and vehicle 126 on the road 114 needs to propagate through the RSU124 and the core network 120 in such a way that a multi-hopcommunication is established. In the IoV environment, vehicular mobilityof the vehicles 126-146 is controlled using a cloud based network (i.e.the cloud network 118). However, in such a case a multi-hopcommunication long latency is obtained which leads to impracticalreal-time control and service by cloud based vehicle control approachusing the cloud network 118. More specifically, the multi-hopcommunication between the cloud network 118 and the vehicle 126 resultsin long delay, which may not be feasible in real-time scenarios.

In addition, on-board control devices of the vehicles, such as thevehicle 146 cannot obtain information about neighboring vehicles (suchas the vehicle 142), pedestrians and environment conditions that are outof their visible range. For instance, the vehicle 146 traveling on theroad 114 intends to pass the intersection 102 after the vehicle 144(that is bigger in size than the vehicle 142) passes the intersection102, and the vehicle 142 (that is a small sized vehicle) is also movinginto the intersection 102. In such a scenario, visibility of the vehicle142 is blocked by the vehicle 144 as shown in FIG. 1. The vehicle 144prevents the vehicle 142 from getting noticed by the vehicle 146 ifcommunication link between the vehicle 142 and the vehicle 146 isaffected or if vehicle 142 and vehicle 146 use different communicationprotocols. As a result, the vehicle 142 and the vehicle 146 mightcollide.

Some embodiments are based on a realization that different communicationtechnologies are utilized to support vehicular communications. Forexample, IEEE Dedicated Short-Range Communications/Wireless Access inVehicular Environments (DSRC/WAVE) standard family for vehicularnetworks, 3GPP Cellular-Vehicle-to-Anything (C-V2X), and the like.However, due to high cost reasons, it is impractical for vehicles (e.g.the vehicles 126-146) to install more than one short range communicationtechnologies which leads to compatibility issues among the vehicle tocommunicate with each other. Therefore, the vehicles equipped with theIEEE DSRC/WAVE cannot communicate with vehicles equipped with the 3GPPC-V2X, and vice versa. Consequently, accuracy of the vehicle mobilitycontrol decision by the on-board control device is severely affected asthe vehicle mobility control decision is based on incomplete informationof the traffic scenario 100.

Some embodiments are based on a realization that IoV-Edge devices (e.g.the RSU 122 and the RSU 124) for controlling vehicular traffic haveadvantages in real-time vehicle mobility control over usage of the cloudnetwork and the on-board device. For example, the IoV-Edge devicesinstalled at intersection or highway merging point directly communicatewith the vehicles (e.g., the vehicles 126-146) approaching theintersection or merging point, the IoV-Edge devices equipped withmultiple communication technologies can communicate with all thevehicles, the IoV-Edge devices are capable to achieve real timecollaboration on vehicle states and environment view via robust highspeed communication links, the IoV-Edge devices are stationary whichenable them in providing reliable communication between the IoV-Edgedevices and the vehicles as well as collecting environmental data havinghigher quality, and the IoV-Edge devices are capable of continuouslymonitoring vehicle traffic and the environment for accurate decisionmaking. Accordingly, the IoV-Edge devices are appropriate to use foroptimal vehicle mobility control decision.

The IoV-Edge devices are implemented based a control point to runcontrol technology for making control decision and provide vehiculartraffic control at the interconnected intersections. Thus, selection ofthe control point is critical in order to perform real time vehiclemobility control. However, controlling vehicle mobility of the vehicles(e.g., the vehicles 126-146) by the cloud network 118 cannot meet thereal time vehicle mobility control due to the multi-hop communicationdelay. In addition, the cloud does not have instant information (i.e.,real-time information) of the vehicles 126-146, pedestrians andcondition of the roads (e.g. the roads 106-116) to make optimaldecision. On-board control does not have comprehensive information tomake optimal vehicle mobility control decision. In addition, theon-board control may also have communication limitation due to theexistence of the heterogeneous vehicular communication technologies suchas the IEEE DSRC/WAVE and the 3GPP C-V2X. The edge devices such as theRSU 122 and the RSU 124 are feasible points to make optimal decision onreal time vehicle control due to their direct communication capability,road condition knowledge, environment view, and real rime collaborationcapability.

As shown in FIG. 1, traffic at the intersection 102 is controlled by theRSU 124 and traffic at the intersection 104 is controlled by the RSU122. In case the vehicle 146 crosses the intersection 102 and intends togo straight towards the intersection 104, the vehicle 138 is required tostop at next intersection (i.e., the intersection 104) due to traffic atthe intersection 104. When the vehicle 138 stops frequently at suchinterconnected intersections, driving experience is hindered and fuelconsumption is increased.

Some embodiments are based on a realization that Artificial Intelligence(AI) techniques are utilized for controlling such traffic at theintersection 102 and the intersection 104. For instance, in a smart-cityinfrastructure with urban traffic management system, traffic at suchintersections may be controlled using AI model based techniques. Someother embodiments are based on realization that the traffic may becontrolled based on inputs provided by a human agent. However, the humanagent based traffic control may not be efficient. The human agent mayexhibit potentially irrational behavior or provide subjective choices,which may be difficult to quantify, calibrate or justify. The humanagent may enhance a traffic control model using data-driven techniques,such as reinforced learning for the traffic control. However, thedata-driven based reinforced learning may not be able to provide aseamless traffic control in abrupt situations or emergency cases. Forexample, the vehicle 146 may stop while crossing the intersection 102due to battery failure of the vehicle 146. This may cause trafficcongestion due to sudden event of the vehicle 146 stopping in theintersection 102. Consequently, traffic congestion occurs at otherneighboring intersections, such as the intersection 104. In order tocontrol the sudden traffic congestion, human involvement may berequired. For instance, the RSU 124 may immediately transmit an alertsignal to a manual operator for the human involvement because the RSU124 has direct view of the intersection 102.

Therefore, some embodiments of the present invention are based onutilization of edge devices to perform edge computing for realizing realtime optimal vehicle control at interconnected intersections (i.e. theintersection 102 and the intersection 104). In this disclosure, the edgedevices such as the RSU 122 and RSU 124, or eNodeB are called as anIoV-Edge. The edge computing for controlling traffic at theinterconnected intersections using the IoV-Edge is described in FIG. 2.

FIG. 2 shows steps of a method 200 for controlling traffic atinterconnected intersections of roads, according to some embodiments.

In some embodiments, an IoV-Edge is placed in proximity of eachintersection (e.g., the intersection 102 and the intersection 104).Initially, the IoV-Edge receives traffic data at the interconnectedintersections at step 202. The IoV-Edge comprises a set of sensors tocollect the traffic data. The traffic data represents states of vehicles(e.g., the vehicles 126-146) approaching an intersection of theinterconnected intersections and directions of the vehicles exiting theintersection. Some examples of the states of the vehicles include, butnot limited to, longitudinal position, velocity, and acceleration of thevehicles. The IoV-Edge then determines weights for the vehicles based onthe directions and traffic at next intersection for a total travel timeand maximum travel time in step 204. The IoV-Edge collects the nextintersection traffic provided by an IoV-Edge of the next intersection.Next, the IoV-Edge minimizes at least one of the total travel time orthe maximum travel time of the vehicles for crossing the intersection byusing different weights for at least two different weights for twodifferent vehicles approaching the intersection, in step 206. Further,the IoV-Edge determines intersection crossing times and velocities ofthe vehicles approaching the intersection based on the minimized totaltravel time or maximum travel time at step 208. At step 210, theintersection crossing times and velocities are transmitted to thevehicles exiting the intersection for controlling the traffic at theinterconnected intersections.

In various embodiments, the IoV-Edge generate road data that includesparts of a road approaching an intersection into sections forcontrolling the interconnected intersections, which is explained nextwith reference to FIG. 3.

FIG. 3 shows a schematic diagram illustrating zone partition inproximity to a road intersection, according to some embodiments. In FIG.3, an IoV-Edge 302 is located at an intersection 300, where theintersection 300 may be interconnected to other intersections (not shownin FIG. 3). Each intersection may be associated with an IoV-Edge.

For instance, interconnected intersections in an urban road environmentare installed with IoV-Edges, as shown in FIG. 3. In such anenvironment, neighboring intersections are connected to a centralIoV-Edge. As shown in FIG. 3, the IoV-Edge 302 is the central IoV-Edgeto other IoV-Edges (such as IoV-Edge 304 and IoV-Edge 306). The IoV-Edge304 and the IoV-Edge 306 are connected to the IoV-Edge 302 through wiredor wireless communication networks. Each of the IoV-Edge 302, theIoV-Edge 304, and the IoV-Edge 306 corresponds to the RSU 122 or RSU 124as shown in FIG. 1. Further, the traffic of each neighboringintersection is controlled by corresponding neighboring IoV-Edgeindependently.

The IoV-Edge 302 generates road data that includes parts of a road 308approaching the intersection 300 into sections (such as a sequencingzone 318 and a control zone 316) to provide safe and optimal control ofvehicular traffic. The road 308 is a one-direction road intersected by anorth bound road (i.e. a road 324) and a south bound road (i.e. a road330), where an intersection area of the road 308 is an intersection or acrossing zone 314. In a similar manner, for a two-lane road 324, thelane that approaches towards the IoV-Edge 302 is partitioned into asequencing zone 322 and a control zone 320 by the corresponding IoV-Edge302.

Further, the IoV-Edge 302 computes an intersection crossing velocity fora vehicle 310 for entering the sequencing zone 318 in order to exit theintersection 300. In the sequencing zone 318, the IoV-Edge 302 computesan intersection crossing time (i.e., optimal time) for the vehicle 310to cross the intersection 300 with minimum delay. The IoV-Edge 302communicates with the neighboring IoV-Edge 304 and vehicles, such as thevehicle 310 that provides the traffic data. The IoV-Edge 302 and theIoV-Edge 304 communicate via communication link 326, which can be wiredor wireless. The IoV-Edge 304 informs the IoV-Edge 302 about arrival ofthe vehicle 310. The vehicle 310 establishes connection with theIoV-Edge 302 when it enters sequence Zone 318 via wireless communicationlink 330. The IoV-Edge 302 then collects state of the vehicle 310 suchas vehicle identifier (ID), location, speed, acceleration, etc. In thecontrol zone 316, location and velocity of the vehicle 310 is controlledto arrive at the crossing zone 314 based on the intersection crossingtime with an intersection crossing velocity.

Further, the IoV-Edge 302 transmits the intersection crossing time andvelocity to the vehicle 310 via wireless communication link 330. Thevehicle 310 determines the optimal motion trajectory to be applied incontrol zone 316 based on the intersection crossing time and velocity.The optimal motion trajectory is transmitted to neighboring vehicle,such as a vehicle 312, and IoV-Edge 302. The vehicle 312 determinescorresponding motion trajectory based on the optimal motion trajectoryof the vehicle 310. In an alternate embodiment, the IoV-Edge 302determines motion trajectory in the control zone 316 for the vehicle 310based on at least one of the optimal arrival time, vehicle location,vehicle speed, vehicle acceleration, speed limit, acceleration limit,headway constraint, or road map. The motion trajectory is determined asa vehicle location, a vehicle velocity, and a value of vehicleacceleration at different time instants, e.g., time to enter the controlzone 316, time to enter the crossing zone 314, and time to exit thecrossing zone 314. Further, the IoV-Edge 302 controls vehicle mobilitybased on the determined motion trajectory.

The intersection crossing time and velocity at which the vehicle 310crosses the intersection 300 prevent the vehicle 310 to stop whilecrossing the intersection 300, and thus improve driving comfort andminimizing fuel consumption. This also allows the vehicle 310 to safelycross the crossing zone 314 without collision. When the vehicle 310exits the crossing zone 314, the IoV-Edge 302 provides information ofnext intersection to the vehicle 310. The IoV-Edge 302 also transmitsinformation of the vehicle 310 to the IoV-Edge 306 via communicationlink 328 (wired or wireless). The information provides details onarrival of the vehicle 310 to the IoV-Edge 306. The IoV-Edges 302 and306 also communicate with the vehicle 310 via the wireless communicationlink 330. This allows the vehicle 310 to provide the information to theIoV-Edges 302 and 306. Examples of the communication link 330 include aDSRC link, a C-V2X link, and the like. Accordingly, the center IoV-Edge302 communicates with both vehicles (e.g. the vehicle 310) andneighboring IoV-Edges (e.g. the IoV-Edges 304 and 306) to make optimalcontrol decision.

Further, to provide the control decision in an efficient manner, theIoV-Edge 302 determines start point of each zone (i.e., the control zone316 and the sequencing zone 318). The IoV-Edge 302 determines length ofeach zone, which is described further with reference to FIG. 4.

FIG. 4 shows a schematic diagram illustrating a length of the sequencingzone 318 and a length of the control zone 316 of the road intersection300, according to some embodiments.

In an illustrative example scenario, when the vehicle 310 with ID ienters the sequencing zone 318 at distance x_(s) from start point of thesequence zone 318 to the crossing zone 314, the vehicle 310 transmits aheartbeat message to the IoV-Edge 302. The heartbeat message containsstate of a vehicle (i.e. the vehicle 310), which further is described inFIG. 5. Upon receiving the heartbeat message from vehicle 310, theIoV-Edge 302 transmits an announcement message to the vehicle 310. Theannouncement message provides information about starting point of thecontrol zone 316 and the crossing zone 314 as well as length of thecrossing zone 314, x_(int). The distance x_(s) 404 is used to the lengthof sequence zone 318, which is a function of a computational speed ofthe IoV-Edge 302. The vehicle 310 then reaches a location x in thesequencing zone 318 at velocity, v_(i)(x). The IoV-Edge 302 computesvelocity for the vehicle 310 to enter the intersection 300, i.e.v_(int,i) and time t_(out,i) for the vehicle 310 to exit the crossingzone 314 of size x_(int). When the vehicle 310 is at location x_(s′),the IoV-Edge 302 transmits a scheduling message to the vehicle 310. Thescheduling message includes the v_(int,i) and time t_(ont,i), which isdescribed further in FIG. 6. When vehicle 310 travels from locationx_(s′) to location x_(c), it determines optimal motion trajectory to beapplied in control zone 316.

The two messages (i.e. the announcement message and the schedulingmessage) have data sizes, K_(x) _(s) and K_(x) _(s′) bits, respectively.These messages are transmitted to the vehicle 310 before the vehicle 310enters the control zone 316 of length x_(c). For a message of size Kbits, wireless transmission time is given by:

$\begin{matrix}{{D_{i}(x)} = \frac{K}{R_{i}(x)}} & (1)\end{matrix}$

where, R_(i)(x) is data rate of a downlink between the IoV-Edge 302 andthe vehicle 310 and defined as

$\begin{matrix}{{R_{i}(x)} = {Blo{g_{2}\left( {1 + \frac{{g_{i}(x)}P_{tx}}{BN_{0}}} \right)}}} & (2)\end{matrix}$

whereg_(i)(x)=β₁x^(−β) ² is channel gain between the vehicle 310 and theIoV-Edge 302 with x being the distance between the vehicle 310 and theIoV-Edge 302,β₁ is path loss constant and β₂ is path loss exponent,P_(tx) is transmission power of the IoV-Edge 302,B is bandwidth of the channel, andN₀ is noise power spectral density.

The IoV-Edge 302 takes computation time t_(s) at the sequencing zone 318for the edge computing. The transmission time of the announcementmessage and the computation time t_(s) satisfies

$\begin{matrix}{{{D_{i}\left( x_{s} \right)} + t_{s}} \leq {\int_{- x_{s}}^{- x_{s^{\prime}}}{\frac{1}{v_{i}(x)}dx}}} & (3)\end{matrix}$

In other words, the sum of the transmission time of the announcementmessage and the computation time t_(s) needs to be less than vehicletravel time from location x_(s) to location x_(s′).

When the vehicle 310 receives the scheduling message from the IoV-Edge302, the vehicle 310 determines an optimal motion trajectory to beapplied in the control zone 316. The vehicle 310 takes a computationtime of t_(c) for computing the optimal motion trajectory. Thetransmission time of the scheduling message and the computation timet_(c) satisfies

$\begin{matrix}{{{D_{i}\left( x_{s^{\prime}} \right)} + t_{c}} \leq {\int_{- x_{s^{\prime}}}^{- x_{c}}{\frac{1}{v_{i}(x)}dx}}} & (4)\end{matrix}$

When the vehicle 310 enters the control zone 316, velocity of thevehicle 310 is controlled to arrive at the crossing zone 314 with theintersection crossing time and velocity determined by the IoV-Edge 302.The vehicles (e.g. the vehicles 310 and 312) adjust their velocitiesaccording to their dynamics model.

Thus, it is essential to determine values of the distance variablesx_(s) 404, x_(s′) 402 and x_(c) 400 such that the constraints in (3) and(4) are satisfied.

To that end, the control zone size must be long enough such that vehiclei 310 can reach any velocity from its control zone entering velocityv_(i)(x_(c)). Therefore, the minimum control zone length for vehicle ican be determined as

${x_{c} \geq {\max\left( {\frac{v_{\max}^{2} - {v_{i}^{2}\left( x_{c} \right)}}{2a_{\max}},\frac{- {v_{i}^{2}\left( x_{c} \right)}}{2a_{\min}}} \right)}}.$

The first term

$\frac{v_{\max}^{2} - {v_{i}^{2}\left( x_{c} \right)}}{2a_{\max}}$

is the distance required to accelerate the vehicle 310 to the maximumvelocity from the velocity v_(i)(x_(c)) using the maximum accelerationa_(max) while the second term

$\frac{- {v_{i}^{2}\left( x_{c} \right)}}{2a_{\min}}$

is the distance needed to stop the vehicle 310 from the velocityv_(i)(x_(c)) using the maximum deceleration a_(min). This resultprovides a lower bound for the control zone length for vehicle 310. Forall vehicle, the IoV-Edge 302 can determine x_(c) such that

$\begin{matrix}{x_{c} \geq {\max\left( {\frac{v_{\max}^{2}}{2a_{\max}},\frac{- {v_{i}^{2}\left( x_{c} \right)}}{2a_{\min}}} \right)}} & (5)\end{matrix}$

Once control zone length x_(c) is decided, x_(s) and x_(s′) aredetermined such that x_(s′)−x_(c) is long enough for (4) to be true. Infact, if (4) is true for vehicles traveling with the maximum velocity,then (4) is true for all vehicles. For the maximum velocity vehicle, (4)becomes

${{D_{i}\left( x_{s^{\prime}} \right)} + t_{c}} \leq {\frac{x_{s^{\prime}} - x_{c}}{v_{\max}}.}$

Therefore, x_(s′) is given by

x _(s′) =x _(c) +v _(max)(D _(i)(x _(s′))+t _(c))  (6)

Once x_(s′) is determined, x_(s) needs to be chosen such thatx_(s)−x_(s′) long enough for (3) to be true. Similarly, if (3) is truefor vehicle traveling with the maximum velocity, then it is true for allvehicles. For the maximum velocity vehicle, (3) becomes

${{D_{i}\left( x_{s} \right)} + t_{s}} \leq {\frac{x_{s} - x_{s^{\prime}}}{v_{\max}}.}$

Therefore, x_(s) is given by

x _(s) =x _(s′) +v _(max)(D _(i)(x _(s))+t _(s))  (7)

Finally, the sequence zone length must be greater than or equal tox_(s)−x_(c).

Each vehicle (e.g., the vehicle 310) optimizes the motion trajectorythat reaches the crossing zone 314 at the intersection crossing timewith the intersection crossing velocity. The vehicle 310 determines anacceleration a_(i) and velocity v_(i) for the control zone 316. Theacceleration is minimized and the vehicle 310 is controlled to arrive atthe crossing zone 314 with the determined intersection crossingvelocity. In the trajectory optimization problem, discrete time systemis applied with T_(s) as the sampling period. The vehicle 310 canoptimize the velocity v=[v_(i)(t₀), v_(i)(t_(n)), . . . , v_(i)(t_(N))]and acceleration a=[a_(i)(t₀), . . . a_(i)(t_(n)), . . . ,a_(i)(t_(N−1))], where t_(n)=nT_(s), ∀n∈[0, N]. In other words, thevehicle determines the optimal velocity and the correspondingacceleration by solving following quadratic programming (QP) problem:

$\begin{matrix}{\min\limits_{v_{i},a_{i}}{\sum\limits_{n = 0}^{N - 1}\left( {\left( {{v_{i}\left( t_{n} \right)} - v_{{int},i}} \right)^{2} + {q{a_{i}(n)}^{2}}} \right)}} & (8)\end{matrix}$

subject to

0≤x _(i)(t _(n))≤x _(i-1)(t _(n))−d _(h) ,∀i,i−1∈I _(d) ,∀n∈[1,N]  (9)

v _(min) ≤v _(i)(t _(n))≤v _(max) ,∀i∈I  (10)

a _(min) ≤a _(i)(t _(n))≤a _(max) ,∀i∈I  (11)

x _(i)(t ₀)=x _(c) ,x _(i)(t _(N))=0,∀i∈I  (12)

v _(i)(t ₀)=v _(0,i) ,v _(i)(t _(N))=v _(int,i) ,∀i∈I  (13)

v _(i)(t _(n))=v _(i)(t _(n-1))+a _(i)(t _(n))T _(s) ,∀n∈[1,N]  (14)

where q is a constant to weight in acceleration, x_(i-1)(t_(n)) is thelocation of the preceding vehicle i−1 at time t_(n) obtained from statusupdate message transmitted by vehicle i−1, d_(h) is the headway safetydriving distance to avoid a forward collision, v_(0,i) is the velocityfor vehicle i to enter the control zone, vehicle i computes motiontrajectory when it close to the control zone, therefore, vehicle i caneither predict its velocity to enter control zone or control itsvelocity to enter control, the reference velocity v_(int,i) can be anyvelocity satisfying traffic policy and traffic condition. By minimizingthe first term of the objective function in (8), the vehicle i willadjust its velocity as close as possible to the reference velocityv_(int,i). By minimizing the second term of the objective function in(8), the vehicle i can reduce the usage of acceleration and smoothmobility. Therefore, the driving comfort can be improved and the fuelconsumption can be reduced.

The condition (9) is safe headway distance constraint, condition (10)shows speed constraint such that vehicle i must follow traffic rule,condition (11) is acceleration constraint for driving comfort and fuelreduction, condition (12) is the location constraint such that motiontrajectory starts from vehicle i enters control zone until to vehicle ienter the crossing zone, condition (13) is the velocity constraint suchthat vehicle i enters control zone with velocity v_(0,i) and enterscrossing zone with reference velocity v_(int,i), and condition (14) isthe velocity and acceleration relationship constraint indicatingvelocity and acceleration are related.

Once the velocity is determined, the location of vehicle i can becalculated by using vehicle dynamics equation as

$\begin{matrix}{{{x_{i}\left( t_{n + 1} \right)} = {{x_{i}\left( t_{n} \right)} + {\frac{{v_{i}\left( t_{n} \right)} + {v_{i}\left( t_{n + 1} \right)}}{2}T_{s}}}},{\forall{n \in \left\lbrack {1,{N - 1}} \right\rbrack}},{\forall{i \in I}}} & (15)\end{matrix}$

The QP problem (8)-(14) and dynamics equation (15) give motiontrajectory of vehicle 310 as (x_(i)(t_(n)), v_(i)(t_(n)),a_(i)(t_(n)))∀n∈[0, N].

FIG. 5 shows an exemplary representation 500 of information of states ofvehicles approaching an intersection and directions of the vehicles,according to some embodiments.

In some embodiments, an IoV-Edge such as the IoV-Edge 302 (shown in FIG.3) receives information of the vehicle state to control traffic atinterconnected intersections (such as the intersection 300), when thevehicle 310 enters a sequencing zone (e.g. the sequencing zone 318). Theinformation of the vehicle state is received through the heartbeatmessage, where the information of the vehicle state includes vehicleinformation 502, a location data 504, a longitudinal position data 506,a velocity data 508, an acceleration data 510, direction information 512and intersection intention 513. The vehicle information 502 includes avehicle identifier (ID). The location data 504 includes current locationof the vehicle 310. The longitudinal position data 506 includeslongitudinal position of the vehicle 310. The velocity and accelerationof the vehicle 310 are provided by the velocity data 508 andacceleration data 510, respectively. The longitudinal position 506 andthe velocity data 508 are used in longitudinal dynamics for controllingto vehicle 310. The intersection intention 513 indicates how vehicle 310plans to cross the intersection, e.g., going straight. The directioninformation 512 corresponds to incoming traffic direction d that can beone four directions, i.e., d∈{n, e, w, s} referring north, east, westand south. The set of vehicles that approach an intersection from north,east, west and south directions are denoted by I_(n), I_(e), I_(w) andI_(s), respectively. Each vehicle is indexed byi∈I=I_(n)∪I_(w)∪I_(w)∪I_(s). Referring back to FIG. 3, for instance, ifthe vehicle 310 moves from south to north, the moving direction of thevehicle 310 is denoted by d(i)=s. The IoV-Edge 302 uses the informationof the vehicle state and direction information for computing theintersection crossing time and velocity for the vehicle 310.

Further, the intersection crossing time and velocity is transmitted tothe vehicle 310, which is shown next in FIG. 6.

FIG. 6 shows an exemplary representation 600 of an intersection crossingtime and velocity for the vehicle 310 exiting the intersection,according to one embodiment.

The intersection crossing time and velocity is transmitted through thescheduling message that includes the vehicle information 502,intersection size 602, traffic direction 604, intersection startlocation 606, intersection exiting time 608, intersection crossing time610, and intersection crossing velocity 612. The intersection size 602corresponds to a size of the crossing zone 314, x_(int) as described inFIG. 4. The traffic direction 604 includes traffic information in thedirection of the vehicle 310. The intersection start location 606 is thelocation where the intersection starts. The intersection exit time 608is the time to exit the intersection. The intersection crossing time 610is the time for vehicle to cross the intersection and intersectioncrossing velocity 612 is the recommended velocity that corresponds to aspeed limit rule for safety requirement, traffic regulations and trafficconditions. The scheduling message also includes information aboutpreceding vehicle.

Referring back to FIG. 3, each IoV-Edge (e.g. the IoV-Edge 302)communicates with each vehicle (e.g. the vehicle 310 and the vehicle312) in the zones 316 and 318 and neighboring IoV-Edges 304 and 306using a zone-based communication protocol. The zone-based communicationprotocol supports transmitting information of the sequencing zone to thevehicle, transmitting a state of the vehicle arriving at the sequencingzone, and transmitting an intersection crossing time and a velocity forexiting the intersection, via the announcement message, the heartbeatmessage and the scheduling message, respectively. The zone-basedcommunication protocol also supports transmitting information aboutpreceding vehicle to the vehicle, transmitting the vehicle motiontrajectory to next vehicle and the IoV-Edge 302, transmitting trafficdata of the intersection to the next intersection, and transmitting zoneinformation of the next intersection to the vehicle. The IoV-Edges alsoexchanges information with the vehicles, which is further described inFIG. 7.

FIG. 7 shows an exemplary representation 700 of information exchangeamong the IoV-Edges and the vehicles crossing the intersection,according to some embodiments. The IoV-Edges include the IoV-Edge 302,the IoV-Edge 304, and the IoV-Edge 306 as described in FIG. 3. Thevehicles include the vehicle 310 and the vehicle 312, which aredescribed in FIG. 3 and FIG. 4. In an illustrative example scenario, thevehicle 310 starts approaching towards the intersection 300. At step702, the IoV-Edge 304 transmits next zone information to the vehicle310. The next zone information includes starting location of sequencingzone of the intersection 300 determined by the IoV-Edge 302. At 704, theIoV-Edge 304 sends traffic data and information about arrival of thevehicle 310 to the IoV-Edge 302. When the vehicle 310 enters thesequencing zone 318, the vehicle 310 sends a heartbeat message to theIoV-Edge 302 at step 706. The heartbeat message includes information ofstatus of the vehicle 310, such as ID, location, velocity andacceleration of the vehicle 310 (as described in FIG. 5). At step 708,the IoV-Edge 302 sends an announcement message to the vehicle 310 uponreceiving the heartbeat message. The announcement message includesinformation of start location of the control zone 316 and start locationof the crossing zone 314 as well as a length of the crossing zone 314.

At step 710, the IoV-Edge 302 performs edge computation to determineintersection crossing time and velocity for the vehicle 310 for crossingthe intersection 300. At step 712, the IoV-Edge 302 transmitsinformation of the intersection crossing time and velocity along withinformation of preceding vehicles to the vehicle 310 via a schedulingmessage. The scheduling message is transmitted before the vehicle 310enters the control zone 316.

At step 714, the vehicle 310 determines an optimal motion trajectory tobe applied in the control zone 316 such that the vehicle 310 exits theintersection 300 at the intersection crossing time and velocity. Thevehicle 310 enters the control zone 316 based on the optimal motiontrajectory. At step 716, the vehicle 310 transmits a status updatemessage containing the optimal motion trajectory to the IoV-Edge 302.The IoV-Edge 302 further transmits to the status update message toneighboring vehicles of the vehicle 310, such as the vehicle 312 fortrajectory planning. The status update message for the vehicle 312 isincluded in a scheduling message to be sent to the vehicle 312 by theIoV-Edge 302, when the vehicle 312 is about to enter the control zone316. The IoV-Edge 302 can also use the status update message to computea global trajectory for the vehicle 310. In this manner, vehiclecollision is avoided. Alternatively, or additionally, the vehicle 310may transmit the status update message to the vehicle 312 for thetrajectory planning. The vehicle 310 exits the crossing zone 314 basedon the intersection crossing time and velocity.

At step 718, the IoV-Edge 302 transmits next zone information of nextintersection to the vehicle 310. At step 720, the IoV-Edge 302 transmitstraffic data and information of the vehicle 310 approaching the nextintersection to the IoV-Edge 306. The traffic data is also transmittedto the IoV-Edge 304. In this manner, vehicle mobility is controlled andcoordinated to enable controlling of vehicles (e.g. the vehicles 310 and312) to pass the interconnected intersections for traffic improvement.

In some embodiments, the IoV-Edges (e.g. the IoV-Edges 302) performstuning of crossing the intersection based on directional weights of thevehicles that reflect interfering traffic at neighboring intersections.The objective of the tuning procedure is to minimize the weighted (totalor maximum) travel time. Thus, the weight is dynamically adaptable,which enable the traffic control to adapt to emergency or abruptscenarios. The determination of the weight for the minimizationprocedure is described further in FIG. 8.

FIG. 8 shows an exemplary schematic diagram depicting determination ofweights of vehicles based on directions of the vehicles for minimizationof at least one of a total travel time or maximum travel time of thevehicles, according to some embodiments. In FIG. 8, an IoV-Edge 812partitions a road 810 connecting to an intersection 800, where the road810 is partitioned into a control zone 806 and a sequencing zone 808. Inthe sequencing zone 808 a vehicle 802 and a vehicle 804 are approachingtowards the intersection 800. The IoV-Edge 812 corresponds to theIoV-Edge 302, the vehicle 802 corresponds to the vehicle 310, and thevehicle 804 corresponds to and the vehicle 312 as described in FIG. 3and FIG. 4. For each vehicle (i.e. the vehicle 802 and 804), a weight isdetermined by the IoV-Edge 812. The weight of each of the vehicle 802and the vehicle 804 is based on the traffic at next intersection, suchas intersection 814 towards a direction of the vehicle 802 (or thevehicle 804) exiting the intersection 800.

In an illustrative example scenario, there is traffic congestion due toa set of vehicles 818-826 at the intersection 814, as shown in FIG. 8.Accordingly, in some embodiments, a weight of the vehicle 802 crossingthe intersection 800 is determined based on traffic at the intersection814. The traffic at the next intersection 814 is formed by motion ofvehicles intersecting the direction of the vehicle 802. NeighboringIoV-Edges (such as an IoV-Edge 816) share traffic information with theIoV-Edge 812. The traffic information includes number of vehicles andtraveling directions of the vehicles at the neighboring intersection814.

The IoV-Edge 812 determines a weighted travel time for the vehicle 802with ID i approaching the intersection 800 in direction d(i) forestimating the intersection crossing time and velocity for the vehicle802. The directional weight for travel time of the vehicle 802 isdetermined based on

$\begin{matrix}{{w_{d{(i)}} = {{\frac{{N - N_{d{(i)}}}\bot}{N}W_{i}} \in \left\lbrack {0,1} \right\rbrack}},{\forall{i \in I}}} & (16)\end{matrix}$

where d(i)^(⊥) is the orthogonal direction with respect to directiond(i),

N is total number of vehicles on all lanes at the next intersection,N_(d(i)) _(⊥) is number of vehicles on the lanes in N_(d(i)) _(⊥)directions at the next intersection, and W_(W) is a physical weightcoefficient to reflect vehicle features.

The w_(d) _((i)) decreases if more vehicles are traveling in d(i)^(⊥)directions at neighboring intersection. The IoV-Edge 812 tends to delaythe exiting time of vehicle having a small w_(d(i)). If the vehicletraveling in d_(i) direction have a small w_(d(i)), more vehicles travelin N_(d(i)) _(⊥) direction at next intersection. Therefore, the vehicleis delayed to arrive at the next intersection so that the neighboringintersection can serve other vehicles in N_(d(i)) _(⊥) directions with apriority. By doing so, the traffic flow of the interconnectedintersections can be improved.

Using directional weight w_(d(i)) for vehicle 812, the travel time isweighted. For example, for vehicle i, if the sequence zone entering timet_(s,i) and the intersection exit time is t_(out,i), the weighted traveltime is w_(d(i))(t_(out,i)−t_(s,i)).

In a similar manner, weight of the vehicle 820 is also determined. Thetotal travel time or the maximum travel time of the vehicles is weightedbased on the determined weight w_(d) _((i)) . The weighted travel timeis minimized by solving a mixed integer linear problem (MILP). TheIoV-Edge 812 solves the MILP as follows:

$\begin{matrix}{\min\limits_{t_{out},B}{\sum\limits_{\forall{i \in I}}\left( {w_{d{(i)}}\left( {t_{{out},i} - t_{s,i}} \right)} \right)}} & (17)\end{matrix}$

subject to

t _(out,i) −t _(s,i) ≥t _(m,i) ,∀i∈I  (18)

t _(out,i+1) −t _(out,i) ≥t _(h) ,∀i∈I _(d)  (19)

t _(out,i) −t _(out,i′) +MB _(i,i′) ≥t _(,int,i) ,∀i,i′∈I,i≠i′  (20)

t _(out,i′) −t _(out,i) +M(1−B _(i,i′))≥t _(int,i) ,∀i,i′∈I,i≠i′  (21)

where I is set of vehicles, t_(out) is the vector consisting oft_(out,i), ∀i∈I, I_(d) is set of vehicles traveling in direction d onsame lane, B_(i,i′)∈{0, 1}, B is the vector of B_(i,i′), ∀i, i′∈I, i≠i′.B_(i,i′)=0 implies that vehicle i is scheduled to cross intersectionprior to vehicle i′ and B_(i,i′)=i implies that vehicle i is scheduledto cross intersection after vehicle i′. The constraint (18) shows thatvehicle i must not violate the speed limit rule from entering thesequence zone to exiting the intersection, i.e., its travel time has alower bound t_(m,i). For example, (x_(s)+x_(int))/v_(max) is a traveltime lower bound. In the constraint (19), headway time t_(h) is appliedto guarantee the safety time gap between two adjacent vehicles on thesame lane. The constraints (20) and (21) guarantee that only one vehiclecan pass the intersection at a time. In particular, the difference ofexit time of any two vehicles i and i′ needs to be greater than thetravel time in the intersection of the preceding vehicle. In (20) and(21), M is an arbitrarily large constant used in a big-M method.

For the weighted maximum travel time objective function, the MILPproblem is formulated as

$\begin{matrix}{\min\limits_{t_{out},B}{\sum\limits_{\forall{i \in I}}\left( {w_{d{(i)}}\left( {t_{{out},i} - t_{s,i}} \right)} \right)}} & (22)\end{matrix}$

The physical weight coefficient Wi of the vehicle can be dynamicallyadapted. Further, weight coefficient of one vehicle differs from weightcoefficient of another vehicle. For example, the vehicle 802 is a motorbike and the vehicle 820 is a truck. The weight coefficient of the truckwill differ from the weight coefficient of the motor bike. The IoV-Edge812 can assign weight coefficient of the truck such that the truck has asmall directional weight w_(d) _((i)) than that of the motor bike. Insuch case, the IoV-Edge 812 delays exit time of truck having the smalldirectional weight. The motor bike that has higher directional weight isprioritized to take over the truck for crossing the intersection 800. Incase the destination of the truck arrives shortly after crossing theintersection 800, the weight coefficient is balanced based on thedestination. The information of balanced weight coefficient is used todetermine intersection crossing time and the velocity of the truck tocross the intersection 800. The truck plans motion trajectory to crossthe intersection 800 based on the intersection crossing time and thevelocity. The planned motion trajectory is updated to the motor bike.

In case of emergency such as an ambulance is crossing the intersection814 in d(i)^(⊥) direction, the IoV-Edge 816 informs the emergencysituation to the IoV-Edge 812. The IoV-Edge 812 performs the weightadjustment based on the emergency traffic, such that the directionalweights of the vehicles 802 and 804 traveling in direction d(i) having asmall weight w_(d) _((i)) . The vehicles 802 and 804 are delayed toarrive at the next intersection 814 so that the neighboring intersection814 can serve ambulance with a priority. This improves traffic flow ofthe interconnected intersections. When the neighboring intersection 814is congested with interfering vehicles, i.e. the vehicles with smallw_(d) _((i)) , the IoV-Edge 812 instantly decreases the incoming trafficby delaying the vehicles to arrive at the next intersection 814. In casethe neighboring intersection 814 has less interfering vehicles, i.e.vehicles with large w_(d) _((i)) , the IoV-Edge 812 schedules thevehicles to arrive at the next intersection 814 without a delay. Thus,the directional weight is dynamic in nature and different vehicles willhave different weights.

FIG. 9 shows an exemplary schematic diagram of controlling traffic atinterconnected intersections of roads based on intersection crossingtimes and velocities of the vehicles, according to some embodiments.

As shown in FIG. 9, there are multiple interconnected intersections 900,902 a, 902 b, 904 a and 904 b. Three intersections 904 a, 900, and 904 bdenoted by west, center and east, respectively are interconnected inparallel. Traffic at the intersections 900, 902 a, 902 b, 904 a and 904b is controlled by IoV-Edges 906, 908, 910, 912 and 914 respectively. Inan illustrative example scenario, traffic in north-south direction atthe intersections 902 a and 902 b is highly congested than traffic ineast-west at the intersections 904 a and 904 b. An IoV-Edge 906 for theintersection 900 controls delays for vehicles (e.g., the vehicle 914)crossing the intersection 900 and traveling towards south-northdirection of the intersection 902 a. To provide an efficient controldecision, the IoV-Edge 906 controls the vehicle 914 such that thevehicle 914 slowdowns its speed while crossing the intersection 900. TheIoV-Edge 906 can minimize directional weight of the vehicle 914 todetermine a minimized weighted travel time of the vehicle 914. TheIoV-Edge 906 then calculates intersection crossing time and velocity forthe vehicle 914 to cross the intersection 900 based on the minimizedweighted travel time on the basis of the vehicle 914 estimates anoptimal motion trajectory. By doing so, the traffic flow of theinterconnected intersections is improved as described in description ofFIG. 3.

FIG. 10 shows a block diagram of a control system 1000 for controllingtraffic at interconnected intersections, according to some embodiments.The control system 1000 is arranged at an IoV-Edge in proximity to acontrol point such as merging and/or intersection of roads. Such anarrangement requires an edge device (i.e. the IoV-Edge 302) comprised ofa set of sensors or be operatively connected to the set of sensors tocollect traffic information in an intersection zone and transmit tovehicles intersection crossing times and velocities before entering acontrol zone to cross the intersection.

The control system 1000 comprises a number of interfaces connecting thecontrol system 1000 with other systems and devices. For example, thecontrol system 1000 comprises a network interface controller (NIC) 1002that is adapted to connect the control system 1000 through a bus 1004 toa network 1006 connecting the control system 1000 with one or moredevices 1008. Examples of such devices include, but not limited to,vehicles, traffic lights, and traffic sensors. Further, the controlsystem 1000 includes a transmitter interface 1010 configured to command,using a transmitter 1012 and the devices 1008 configured to transmitcommands the vehicles to moved based on trajectories that correspond tointersection crossing times and velocities determined by the processor1014. Through the network 1006, the control system 1000 receives trafficdata 1032 using a receiver interface 1028 connected to a receiver 1030,the system 1000 can receive traffic information in the intersection zoneas well as traffic information in neighboring intersections. The trafficdata includes information of states (e.g., acceleration, location,velocity) of vehicles approaching an intersection of the interconnectedintersections and directions of the vehicles exiting the intersection.Additionally, or alternatively, the control system 1000 includes acontrol interface 1034 configured to transmit commands to the one ormore devices 1008 to change their respective state, such asacceleration, velocity, and the like. The control interface 1034 may usethe transmitter 1012 to transmit the commands and/or any othercommunication means.

In some implementations, a human machine interface (HMI) 1040 within thesystem 1000 connects the control system 1000 to a keyboard 1036 andpointing device 10384, wherein the pointing device 1038 can include amouse, trackball, touchpad, joy stick, pointing stick, stylus, ortouchscreen, among others. The control system 1000 can also be linkedthrough the bus 1004 to a display interface adapted to connect thecontrol system 1000 to a display device, such as a computer monitor,camera, television, projector, or mobile device, among others. Thecontrol system 1000 can also be connected to an application interfaceadapted to connect the control system 1000 to one or more equipment forperforming various power distribution tasks.

The control system 1000 includes the processor 1014 configured toexecute stored instructions, as well as a memory 1016 that storesinstructions that are executable by the processor 1014. The processor1014 can be a single core processor, a multi-core processor, a computingcluster, or any number of other configurations. The memory 1016 caninclude random access memory (RAM), read only memory (ROM), flashmemory, or any other suitable memory systems. The processor 1014 isconnected through the bus 1004 to one or more input and output devices.These instructions implement a method for adaptive control of vehiculartraffic at interconnected intersections.

To that end, the system 1000 includes a traffic configuration 1018. Forexample, the traffic configuration 1018 includes a structure of a zoneof intersection of a road. In some embodiments, the structure of theintersection zone includes a sequencing zone and a control zone. Eachzone includes information of sections of multiple roads on which thevehicles are moving towards the intersection. In such a manner, thetraffic configuration 1018 allows the control system 1000 to controlvehicles (e.g., the vehicles 310, 312, 802, 804 and 914) in differentzones (e.g., the zones 316, 318, 329, 322, 806 and 808).

The control system 1000 includes a weight determining module 1020configured to determine weights for travel times of vehicles approachingthe intersection, while traveling within a sequencing zone of theintersection, as described in FIG. 8. The weight determining module 1020is further configured to minimize the weighted travel times of thevehicles. In one example embodiment, the weight determining module 1020solves a MILP for the minimization procedure. After the minimization,the minimized weighted travel time is provided to crossing time andvelocity determining module 1022. The crossing time and velocitydetermining module 1022 determines intersection crossing times andvelocities for the vehicles based on the minimized values.

The control system 1000 includes a trajectory planner and tracker 1024configured to determine states of the vehicles traveling within thesequencing and control zones. The trajectory planner and tracker 1024 isalso configured to solve the optimal trajectory problem for determiningmotion trajectories for different vehicles according to the sequentialarrival on the intersection while optimizing a metric of performance,such as energy consumption of the vehicles. For instance, the trajectoryplanner and tracker 1024 accesses motion trajectories of a vehicle (suchas the vehicle 310) that are determined based on the intersectioncrossing times and velocities for the vehicle 310. The trajectoryplanner and tracker 1024 transmits the motion trajectories of thevehicle 310 to neighboring vehicles (such as the vehicle 312) so as toplan motion trajectory to cross the intersection (i.e. intersection 300)without colliding with each other. The traffic configuration 1018, theweight determining module 1020, the crossing time and velocitydetermining module 1022 and the trajectory planner and tracker 1024 arestored in storage 1026.

FIG. 11A shows a schematic of a vehicle 11000 including a controller1102 in communication with control system, according to someembodiments.

The vehicle 1100 can be any type of wheeled vehicle, such as a passengercar, bus, or rover. Further, the vehicle 1100 can be an autonomousvehicle or a semi-autonomous vehicle.

In some implementations, motion of the vehicle 1100 is controlled. Forexample, lateral motion of the vehicle 1100 is controlled by a steeringsystem 1112 of the vehicle 1100. In one embodiment, the steering system1112 is controlled by the controller 1102. Additionally, oralternatively, the steering system 1112 can be controlled by a driver ofthe vehicle 1100.

Further, the vehicle 1100 includes an engine 1106, which may becontrolled by the controller 1102 or by other components of the vehicle1100. The vehicle 1100 may also include one or more sensors 1104 tosense surrounding environment of the vehicle 1100. Examples of thesensors 1104 include, but are not limited to, distance range finders,radars, lidars, and cameras. The vehicle 1100 may also include one ormore sensors 1110 to sense current motion quantities and internalstatus, such as steering motion of the vehicle 1100, wheel motion of thevehicle 1100, and the like. Examples of the sensors 1110 include, butare not limited to, a global positioning system (GPS), accelerometers,inertial measurement units, gyroscopes, shaft rotational sensors, torquesensors, deflection sensors, a pressure sensor, and flow sensors. Thevehicle 1100 may be equipped with a transceiver 1108 enablingcommunication capabilities of the controller 1102 through wired orwireless communication channels with control system (e.g., the controlsystem 1000). For example, through the transceiver 1108, the controller1102 receives the motion trajectory, and controls actuators and/or othercontrollers of the vehicle according to the received trajectory in orderto control mobility of the vehicle 1100.

FIG. 11B shows a schematic of interaction between a set of control units1120 of the vehicle 1100 for controlling vehicular mobility, accordingto some embodiments. For example, in some embodiments, the set ofcontrol units 1120 of the vehicle 1100 includes a steering control unit1122 and brake/throttle control unit 1124 that control rotation andacceleration of the vehicle 1100. In such a case, the controller 1102outputs control inputs to the control unit 1122 and the control unit1124 to control the state of the vehicle 1100. Further, the set ofcontrol units 1120 may also include high-level controllers, e.g., alane-keeping assist control unit 1126 that processes the control inputsof the controller 1102. In both cases, the set of control units 1120utilizes outputs of the controller 1102 to control at least an actuatorof the vehicle 1100 (such as the steering wheel and/or the brakes of thevehicle 1102) in order to control the motion of the vehicle 1100.

FIG. 11C shows a schematic for determining optimal trajectory 1132 for avehicle 1134, according to some embodiments. The intersection 1130 isequipped with an IoV-Edge 1140, where the IoV-Edge corresponds to theIoV-Edge 302 as described in FIG. 3. The IoV-Edge 1140 providesintersection crossing time and velocity for the vehicle 1132 to crossthe intersection 1130, as described in description of FIG. 4. Thevehicle 1132 on road 1138 generates trajectory that aims to keep thevehicle 1132 within particular road bounds, and aims to avoid otheruncontrolled vehicles, i.e., vehicles 1134 and 1142. In someembodiments, each of the vehicle 1134 and the vehicle 1142 can berepresented by one or multiple inequality constraints in a time or spaceformulation of the mixed-integer optimal control problem, including oneor multiple additional discrete variables for each of the obstacles. Forexample, based on embodiments configured to implement a mixed-integermodel predictive controller, the autonomous or semi-autonomouscontrolled vehicle 1132 can make discrete decisions in real time suchas, e.g., pass another vehicle on the left or on the right side orinstead to stay behind another vehicle (i.e. the vehicle 1134) withinthe current lane of the road 1138.

In some embodiments, to control the vehicle 1132, the control inputsinclude commands specifying values of one or combination of a steeringangle of the wheels of the vehicle 1132 and a rotational velocity of thewheels, and the measurements include values of one or combination of arotation rate of the vehicle 1132 and an acceleration of the vehicle1132. Each state of the vehicle 1132 includes a velocity and a headingrate of the vehicle 1132, such that the motion model relates the valueof the control inputs to a first value of the state of the vehicle 1132through dynamics of the vehicle 1132 at consecutive time instants, andthe measurement model relates the value of the measurement to a secondvalue of the state of the vehicle 1132 at the same time instant.

FIG. 12 shows a graphical representation 1200 depicting comparison of amaximum travel time with respect to an intersection crossing velocityand a maximum travel time with respect to an initial velocity of avehicle entering control zone, according to some embodiments.

To define a baseline, a scheduling scheme based on first come firstserve (FCFS) basis is adopted. The FCFS schedule time for vehicles toexit an intersection in an order. In an illustrative example, a vehicle,such as vehicle 804 of FIG. 8 enters the control zone 806 which isfollowed by the vehicle 802. In case of FCFS, the vehicle 802 isscheduled to cross the intersection 800 after the vehicle 804 exits theintersections. In case of scheduling based on MILP, vehicular mobilityof the vehicles 802 and 804 is controlled by minimizing weighted maximumtravel time of the vehicles 802 and 804, where the minimizationprocedure is described in description of FIG. 8. In FIG. 8, thevehicles, i.e. the vehicles 802 and 804 are travelling in northdirection and south direction from 7 m/s to 10 m/s and the directionalweight w_(d) _((i)) =1.

As shown in FIG. 12, difference, between the maximum travel time withrespect to the intersection crossing velocity and the maximum traveltime with respect to the initial velocity based on MILP and FCFS,decreases as intersection crossing velocity or initial velocityincreases. The MILP and FCFS scheduling are identical when theintersection crossing velocity and the initial velocity are equal forfour directions. This is due to that FCFS scheduling yields an optimalsolution when a vehicle (i.e. the vehicle 802 or 804) has the identicalinitial velocity and intersection crossing velocity. However, if thevehicles 802 and 804 have different initial velocity or intersectioncrossing velocity, FCFS scheduling is not optimal. Therefore, the MILPscheduling achieves shorter maximum travel time than the FCFS schedulingdoes. For instance, MILP scheduling can decrease the maximum travel timeby up to 4.7% if the vehicles 802 and 804 traveling in the north andsouth directions have the initial velocity of 7 m/s while the vehiclesin the east and west directions have the initial velocity of 10 m/s.

FIG. 13 shows a graphical representation 1300 depicting impact ofweighting parameter in controlling traffic at interconnectedintersections, according to some embodiments.

In FIG. 13, a total travel time of vehicles (e.g., the total time oftravel of the vehicle 802 and the vehicle 804 as described indescription of FIG. 8) in each direction for the different weightingvalues with an initial velocity and a reference velocity of 7 m/s and 8m/s, respectively. The weighting values of the vehicle 802 and thevehicle 804 in north and south directions vary from 0.1 to 1, whilew_(d∈{e,w)}=1, and |I_(d)|=4. It is observed that the total travel timeof the vehicles in the north and south directions, i.e., Σ_(i∈I) _(s∪In)(t_(out)−t₀), increases when w_(d∈{n,s}) decreases. In this case, asmall value of w_(d∈{n,s}) implies that next intersection is highlycongested. Therefore, the vehicles traveling in north and south aredelayed based on small weight w_(d∈{n,s}), thus reducing the travel timeof the vehicles in east and west directions. For instance, the gapbetween the vehicles in different directions, in terms of the totaldelay, can be roughly 63.3% when=w_(d∈{n,s})=0.1 and v_(0,i)=v_(int,i)=7m/s.

FIG. 14 shows a graphical representation 1400 depicting impact ofneighboring intersections in controlling traffic at interconnectedintersections, according to some embodiments.

The neighboring intersections may correspond to the neighboringintersection 814 as described in description of FIG. 8. The graphicalrepresentation 1400 shows a maximum travel time and a total travel timeof vehicles (e.g., the vehicle 802 and the vehicle 804) at anintersection (e.g., the intersection 800) during two consecutivesimulation runs. The curves for maximum travel time and the total traveltime are the plot of left-axis and right y-axis, respectively. It isshown in the FIG. 14, that the travel time decreases at velocity v₀ andincreases at velocity v_(int). Further, it is shown in the FIG. 14 thatthe maximum travel time and the total travel time in a second simulationround are reduced compared with a first simulation round. This is due tothe fact that a small weighting value is applied to the vehiclestraveling in the west and east directions. The vehicles with a smallweighting value are delayed to exit the intersection in the firstsimulation round. When the vehicles arrive at the neighboringintersections, their late arrivals enable the neighboring IoV-Edges(e.g., the IoV edge 816) to schedule the vehicles in north and southdirections with a priority. Therefore, the maximum travel time and totaltravel time of the vehicles at the next intersection can be reduced inthe second simulation round. For example, the total travel time in thefirst simulation round can decrease by up to 14.3% in the secondsimulation round when v₀=v_(int)=7 m/s.

EMBODIMENTS

The following description provides exemplary embodiments only, and isnot intended to limit the scope, applicability, or configuration of thedisclosure. Rather, the following description of the exemplaryembodiments will provide those skilled in the art with an enablingdescription for implementing one or more exemplary embodiments.Contemplated are various changes that may be made in the function andarrangement of elements without departing from the spirit and scope ofthe subject matter disclosed as set forth in the appended claims.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, understood by one ofordinary skill in the art can be that the embodiments may be practicedwithout these specific details. For example, systems, processes, andother elements in the subject matter disclosed may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known processes,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments. Further, like referencenumbers and designations in the various drawings indicated likeelements.

Also, individual embodiments may be described as a process which isdepicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart may describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be re-arranged. A process may be terminated when itsoperations are completed, but may have additional steps not discussed orincluded in a figure. Furthermore, not all operations in anyparticularly described process may occur in all embodiments. A processmay correspond to a method, a function, a procedure, a subroutine, asubprogram, etc. When a process corresponds to a function, thefunction's termination can correspond to a return of the function to thecalling function or the main function.

Furthermore, embodiments of the subject matter disclosed may beimplemented, at least in part, either manually or automatically. Manualor automatic implementations may be executed, or at least assisted,through the use of machines, hardware, software, firmware, middleware,microcode, hardware description languages, or any combination thereof.When implemented in software, firmware, middleware or microcode, theprogram code or code segments to perform the necessary tasks may bestored in a machine readable medium. A processor(s) may perform thenecessary tasks.

Various methods or processes outlined herein may be coded as softwarethat is executable on one or more processors that employ any one of avariety of operating systems or platforms. Additionally, such softwaremay be written using any of a number of suitable programming languagesand/or programming or scripting tools, and also may be compiled asexecutable machine language code or intermediate code that is executedon a framework or virtual machine. Typically, the functionality of theprogram modules may be combined or distributed as desired in variousembodiments.

Embodiments of the present disclosure may be embodied as a method, ofwhich an example has been provided. The acts performed as part of themethod may be ordered in any suitable way. Accordingly, embodiments maybe constructed in which acts are performed in an order different thanillustrated, which may include performing some acts concurrently, eventhough shown as sequential acts in illustrative embodiments. Further,use of ordinal terms such as “first,” “second,” in the claims to modifya claim element does not by itself connote any priority, precedence, ororder of one claim element over another or the temporal order in whichacts of a method are performed, but are used merely as labels todistinguish one claim element having a certain name from another elementhaving a same name (but for use of the ordinal term) to distinguish theclaim elements.

Although the present disclosure has been described with reference tocertain preferred embodiments, it is to be understood that various otheradaptations and modifications can be made within the spirit and scope ofthe present disclosure. Therefore, it is the aspect of the append claimsto cover all such variations and modifications as come within the truespirit and scope of the present disclosure.

We claim:
 1. A traffic control system for controlling traffic atinterconnected intersections of roads, comprising: a receiver configuredto receive traffic data indicative of states of vehicles approaching anintersection of the interconnected intersections and directions of thevehicles exiting the intersection; a processor configured to determineintersection crossing times and velocities of vehicles approaching theintersection by minimizing at least one of a total travel time or amaximum travel time of the vehicles for crossing the intersection,wherein a contribution of each vehicle of the vehicles approaching theintersection in the at least one of a total travel time or a maximumtravel time is weighted based on directions of the vehicles and trafficat next intersection along the directions, such that the minimizationuses different weights for at least two different vehicles of thevehicles approaching the intersection; and a transmitter configured totransmit the intersection crossing times and velocities to the vehiclesexiting the intersection for controlling the traffic at theinterconnected intersections.
 2. The traffic control system of claim 1,wherein the traffic is controlled based on motion trajectories of thevehicles for exiting the intersection, the motion trajectories aredetermined based on the intersection crossing times and velocities ofthe vehicles approaching the intersection.
 3. The traffic control systemof claim 2, wherein the processor is configured to partition the roadsapproaching the intersection into sections based on the traffic data,the sections comprising a control zone adjacent to the intersection anda sequencing zone adjacent to the control zone, wherein the intersectioncrossing times and velocities of the vehicles are determined in thesequencing zone and wherein the motion trajectories for the vehicles aredetermined to travel in the control zone.
 4. The traffic control systemof claim 3, wherein the processor is configured to minimize the totaltravel time of vehicles or the maximum travel time of vehicles in thesequencing zone with the intersection crossing times and velocities ofthe vehicles fixed in the control zone.
 5. The traffic control system ofclaim 4, wherein the processor performs the minimization by solving amixed integer linear problem (MILP), the MILP simulates motion of thevehicles crossing the intersection subject to a reference velocitycorresponding to safety requirement and traffic regulation.
 6. Thetraffic control system of claim 3, wherein the processor is configuredto determine length of the sequencing zone and length of the controlzone, the sequencing zone length is a function of a computational speedof the processor and wherein the control zone length corresponds to adistance needed to decelerate a maximum allowed speed of a vehicleentering the control zone or accelerate the vehicle from a control zoneentering velocity to the maximum allowed speed.
 7. The traffic controlsystem of claim 3, wherein the traffic control system exchangesinformation with each vehicle of vehicles in the sequencing zone and thecontrol using a zone-based communication protocol comprising:transmitting information of the sequencing zone to the vehicle;receiving a state of the vehicle arriving at the sequencing zone, thestate of the vehicle comprising a location of the vehicle, a velocity ofthe vehicle and an acceleration of the vehicle; transmitting anintersection crossing time and velocity for exiting the intersection tothe vehicle before entering the control zone; transmitting informationabout preceding vehicle to the vehicle; receiving motion trajectory ofthe vehicle to be applied in the control zone; transmitting the vehiclemotion trajectory to next vehicle; transmitting traffic data of theintersection to the next intersection; and transmitting zone informationof the next intersection to the vehicle
 8. The traffic control system ofclaim 1, wherein the weight of the vehicle is based on the traffic atthe next intersection towards a direction of the vehicle exiting theintersection.
 9. The traffic control system of claim 8, wherein thetraffic at the next intersection is traffic formed by motion of vehiclesintersecting the direction of the vehicle.
 10. The traffic controlsystem of claim 9, wherein directional weight of a vehicle i isdetermined according to$w_{d{(i)}} = {\frac{N - N_{{d{(i)}}^{\bot}}}{N}W_{i}}$ wherein d(i) isdirection of the vehicle approaching the intersection; N is total numberof vehicles at the next intersection after crossing the intersection;d(i)^(⊥) is direction perpendicular to the direction d(i); N_(d(i)) ^(⊥)is number of vehicles in the direction d(i)^(⊥) at the next intersectionafter crossing the intersection; and W_(i) is a physical weightcoefficient to reflect vehicle features.
 11. The traffic control systemof claim 1, wherein the traffic control system is arranged on an edgedevice placed in proximity of each intersection of the interconnectedintersections, the edge device comprises a set of sensors to collect thetraffic data, wherein the processor is configured to perform thedetermination of the intersection crossing times and velocities of thevehicles entering the intersection and the minimization based on thetraffic data.
 12. A method for controlling traffic at interconnectedintersections of roads, wherein the method uses a processor coupled to areceiver configured to receive traffic data indicative of states ofvehicles approaching an intersection of the interconnected intersectionsand directions of the vehicles exiting the intersection, wherein theprocessor is coupled with stored instructions implementing the method,wherein the instructions, when executed by the processor carry out stepof the method, comprising: determining intersection crossing times andvelocities of the vehicles approaching the intersection by minimizing atleast one of a total travel time or a maximum travel time of thevehicles for crossing the intersection, wherein a contribution of eachvehicle of the vehicles approaching the intersection in the total traveltime or the maximum travel time is weighted based on the directions ofthe vehicles and traffic at next intersection, such that theminimization uses different weights for at least two different vehiclesof the vehicles approaching the intersection; and transmitting theintersection crossing times and velocities to the vehicles exiting theintersection via a transmitter coupled to the processor for controllingthe traffic at the interconnected intersections.
 13. The method of claim12, wherein the traffic is controlled based on motion trajectories ofthe vehicles for exiting the intersection, the motion trajectories aredetermined based on the intersection crossing times and velocities ofthe vehicles approaching the intersection.
 14. The method of claim 13,further comprising: partitioning the roads approaching the intersectioninto sections based on the traffic data, the sections comprising acontrol zone adjacent to the intersection and a sequencing zone adjacentto the control zone, wherein the intersection crossing times andvelocities of the vehicles are determined in the sequencing zone subjectto intersection crossing times and velocities of the vehicles in thecontrol zone.
 15. The method of claim 14, further comprising: minimizingthe total travel time or the maximum travel time of vehicles in thesequencing zone with the intersection crossing times and velocities ofthe vehicles fixed in the control zone.
 16. The method of claim 15,wherein the minimization comprising: solving a mixed integer linearproblem (MILP), the MILP simulates motion of the vehicles crossing theintersection subject to a reference velocity corresponding to safetyrequirement and traffic regulations.
 17. The method of claim 14, whereinpartitioning the roads comprising determining length of the sequencingzone and length of the control zone, wherein the sequencing zone lengthis function of a computational speed of the processor and wherein thecontrol zone length corresponds to a distance needed to decelerate amaximum allowed speed of a vehicle entering the control zone oraccelerate the vehicle from a control zone entering velocity to themaximum allowed speed.
 18. The method of claim 14, further comprisingexchanging information with each vehicle of vehicles in the sequencingzone and the control zone using a zone-based communication protocolcomprising: transmitting information of the sequencing zone to thevehicle; receiving a state of the vehicle arriving at the sequencingzone, the state of the vehicle comprising a location of the vehicle, avelocity of the vehicle and an acceleration of the vehicle; transmittingan intersection crossing time and a velocity for exiting theintersection to the vehicle before entering the control zone;transmitting information about preceding vehicle to the vehicle;receiving motion trajectory of the vehicle to be applied in the controlzone; transmitting the vehicle motion trajectory to next vehicle;transmitting traffic data of the intersection to the next intersection;and transmitting zone information of the next intersection to thevehicle.
 19. The method of claim 13, wherein the weight of the vehicleis based on the traffic at the next intersection towards a direction ofthe vehicle exiting the intersection, the traffic at the nextintersection is traffic formed by motion of vehicles intersecting thedirection of the vehicle and wherein directional weight of a vehicle iis determined according to$w_{d{(i)}} = {\frac{N - N_{{d{(i)}}^{\bot}}}{N}W_{i}}$ wherein d(i) isdirection of the vehicle approaching the intersection; N is total numberof vehicles at the next intersection after crossing the intersection;d(i)^(⊥) is direction perpendicular to the direction d(i); N_(d(i)) ^(⊥)is number of vehicles in the direction d(i)^(⊥) at the next intersectionafter crossing the intersection; and W_(i) is a physical weightcoefficient to reflect vehicle features.
 20. A non-transitory computerreadable storage medium embodied thereon a program executable by aprocessor for performing a method, the method comprising: receivingtraffic data indicative of states of vehicles approaching anintersection of the interconnected intersections and directions of thevehicles exiting the intersection; determining intersection crossingtimes and velocities of the vehicles approaching the intersection byminimizing at least one of a total travel time or a maximum travel timeof the vehicles crossing the intersection, wherein a contribution ofeach vehicle in the total travel time or the maximum travel time isweighted based on the directions of the vehicles and traffic at nextintersection, such that the minimization uses different weights for atleast two different vehicles of the vehicles approaching theintersection; and transmitting the intersection crossing times andvelocities to the vehicles exiting the intersection via a transmittercoupled to the processor, for controlling the traffic at theinterconnected intersections.